Characteristics of sandstone reservoirs including porosity and permeability are of great importance to the petroleum industry. The prediction of these characteristics in the absence of measured (or hard) data is of great economic value because these data are used to evaluate the economic viability of hydrocarbon production facilities. Process-based approaches for simulating porosity and permeability are designed to predict pore-structure evolution as a result of the physical characteristics (such as grain size and grain composition) of the original sediment and the environmental conditions that the sediment is subjected to after deposition.
A number of workers (such as, Lander and Walderhaug, 1999; Bonnell et al., 1999) have presented zero-dimensional process-based models for predicting reservoir quality characteristics of sandstones. These models are very powerful and have been used to accurately predict static bulk properties such as cement abundance, average compaction, and porosity. Unfortunately, these models are of limited use for predicting permeability, since the flow characteristics of a porous medium are related not only to the porosity, but the three-dimensional relations (connectivity) between pores and the roughness and individual shape of pores.
Øren and Bakke (2002) published a study in which they outline a process for reconstruction of the sandstones and prediction of the transport properties of the sandstones. However, the authors admit, “It is unclear how accurately it can reproduce more heterogeneous and diagentically complex sandstones such as those often encountered in the oil industry.” Accordingly there is a need for a more accurate process using more sophisticated algorithms for predicting compaction, cementation, and permeability. This invention satisfies that need.
Recently, Dillon et al. (2004) published a study in which they simulate porosity and permeability evolution via manipulation of sandstone images. The authors are able to more realistically model diagenetic evolution than Oren and Bakke (2002). Their methodology relies heavily on sandstone thin-section image processing; as such, they (a) require rock samples in order to begin their analysis, and (b) are limited to two-dimensional space, whereas true sandstone pore networks are three-dimensional.